Programming Guides

Languages

Bash and Python are among the most commonly used programming languages in data science. To run your pipelines and analyze data effectively, you'll likely need to learn or enhance your skills in these languages. Markdown is another language commonly used in science to present results and create documentation. The links provided here offer comprehensive guides tailored for both beginners learning the basics and advanced users seeking specific topics and practical how-tos.

Bash guide: This guide covers a broad range of topics, from fundamental concepts like file manipulation, arithmetic operations, iteration, condition testing, and working with arrays to more advanced topics such as using databases, managing permissions, and working with remote or cluster resources.

Python guide: This guide provides everything you need to know to become a Python programmer capable of analyzing, manipulating, and visualizing large datasets. Key topics widely used in data science are covered, including modules such as NumPy, pandas, SciPy, Django, and the Matplotlib library. You'll also find information on machine learning, working with databases, and much more.

Markdown Guide: This is a comprehensive guide that covers everything from how does it work and its most common applications to explanations of basic and extended syntax and some useful hacks.

Tools

Jupyter Guide: In this guide we explain you how to start using Jupyter in OnDemand, how to use languages other than python within your notebook, visualize documents, images and videos, export variables, and a couple other tips and tricks that will be very useful for beginners and more advanced users.